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I think most AI governance conversations stop too early.

Teams talk about dashboards, usage charts, and prompt capture. Those are useful, but they are not the same thing as a trustworthy record.

The harder problem is this: if someone asks you six months later whether a block of code was AI-generated, can you prove the record still means what it said when it was created?

That is why we added two things in LineageLens: a provenance hash chain and a signed AI BOM export.

1. I've always been on the personal brand side. More and more founders are building it now (sometimes even before the product is ready while it's still in development, before seed fundraising). The CEO builds their position so the product sells more easily at the official launch.

2. But I have experience with people who built the product, scaled it, and only then did we discover who was behind it.

Honestly, with the first approach, I'd be concerned that people invest more in me as a person than in the product. People would idealise the founder and overlook the product's flaws (which could hurt development and constructive feedback).

+ I noticed the most common mistake that many people who started building a personal brand first, connected their product to their personal accounts (emails, social media, etc.) and started having a problem selling these things, because they cannot "give someone keys" to their personal profiles.

I've been running Claude Code, Cursor, and Codex pretty heavily for the last few months and I keep hitting the same loop:

1. Start a task in one agent

2. Switch to something else (Slack, Twitter, another terminal)

We teamed up with Vercel and we want you shipping this Friday.
May 15 is Vercel Day. Launch your product on Product Hunt that day with the Vercel Day tag and you're on the official Vercel Day leaderboard alongside every other builder going live that day. Top launches win prizes and get serious visibility from a crowd that's already paying attention.
This is the move if you've been waiting for a reason to launch. You've got four days.
What are you building? Drop it below

Everyone is talking about Karpathy's LLM knowledge base. 16 million views in a few weeks. But nobody is naming the actual problem it reveals.
Karpathy built a personal wiki, roughly 100 articles and 400,000 words, compiled entirely by an AI from his raw research docs. He didn't write a single word of it. It's one of the most compelling demos of what personal AI knowledge infrastructure can look like.
But here's the problem: The wiki lives on his machine. Every time he opens a different AI tool, it knows nothing about it.
That's not a Karpathy problem. That's everyone's problem.
Some other people like Allie Miller built a "context vault," structured summary docs she feeds into Claude to avoid re-explaining herself every session. Teams are using Notion as a memory layer. Developers are writing custom scripts to sync context across tools. Some are setting up github wikis, while some are using Obsidian to set up local markdown files.
All brilliant. All manual. All solving the same root failure: AI has no durable, portable sense of who you are.
Every time you open Claude, Cursor, ChatGPT, or whatever ships next month, it starts from zero. There is no standard for "this is who I am" that travels with you. The burden of continuity falls entirely on the user.
Some will point to Claude's memory feature or ChatGPT's memory. These are genuine steps forward. But they are per-platform, opaque, and you don't own them. Switch tools and you start over. They are not solving the
portability problem. They are each building their own silo of you.
The real issue is that context continuity is being treated as a feature when it is actually infrastructure.
Features get built per-product. Infrastructure sits beneath products. What's missing is a memory layer that sits above any individual tool. One canonical store of your context, preferences, and knowledge that you own, control, and selectively grant any AI permission to read. Not stored by Anthropic. Not stored by OpenAI. Portable across the fragmented AI landscape.
A year ago the coordination problem here would have been enormous. Getting AI providers to query an external context layer would have required deals, standards negotiations, and a lot of waiting.
MCP changed that. MCP (Model Context Protocol) is an open standard that lets AI tools query external data sources directly. Any AI tool that supports MCP can already read from an external context server, no special agreements with providers required. The infrastructure exists. What has been missing is someone building the right thing on top of it: a user-owned, canonical context store that any MCP-compatible AI can read with your permission. For example, with the AICF MCP Server, you could not only read but directly add, edit, delete, organize your cross-platform memories from within the AI chats.
Our vision: One memory. Every AI. Owned by you.
p.s. we have a lifetime deal going on AppSumo which will end this month: https://appsumo.8odi.net/m4n0da

I built a tool to find out if I should become a CEO. It said no. So I'm doing the next best thing and launching a product instead.

Real talk, I could write a normal maker comment explaining what uselink does. Instead I'm going to let you click on something.

Hey Trophy community!
Since we launched Trophy 1.0 back in January we've been hard at work solving the most difficult challenges with running backend gamification infrastructure at scale.
Today we're excited to turn our attention to the frontend part of the picture with the launch of our open-source UI library.

Appreciate any support!

Hey everyone!
Just shipped A/B testing for thumbnails and hooks directly into OpenCut AI. Generate variants, score them automatically, and pick the winner all inside the editor, no external tools needed.
Thumbnail A/B Testing
Generate up to 4 thumbnail variants, then auto-score each one on:
- Contrast
- Text readability
- Face presence
- Color vibrancy
- Composition
Each variant gets a letter grade (A-F) and an overall score. The winner gets a "Recommended" badge. You can also score thumbnails you've already generated.
Hook A/B Testing
Got a transcript? Generate 5 hook variants different openings designed to grab attention in the first 3 seconds. Each variant gets an estimated engagement score so you can compare them side by side.
Analytics Dashboard
Tracks your scoring history over time:
- Average composite score
- Grade distribution
- Per-signal breakdown (hook, curiosity, energy, etc.)
- Strongest and weakest signals
- Score trend chart
Why this matters
TubeBuddy puts A/B testing behind their $15/mo Legend plan. VidIQ does the same. We think this should be free and built into your editor not a separate subscription.
Everything runs locally. No data sent to third parties. MIT licensed.
What other YouTube optimization features would you want built in?

Building Hayley has meant reading a lot of research on how people process decisions out loud.

The patterns that come up most: a relationship that needs a conversation you keep avoiding. A business decision that's already made but won't stop reopening. A version of yourself you're not sure you're living up to.

Curious what it is for the people in this community. Not looking for details, just the category.

For Bitgrain, we found that maximum users came from blog features! yes and to be exact, the new users spiked upto 8.8k on a single day (that was the highest) and all came from blogs! Currently the max source is organic search and ai responses.
Which channel is working the best for your brand?

A lot of people try to raise funding before they even begin.

And then sometimes I read those zero to hero stories. (Maybe they re a bit "polished" by the media to have publicity.)

In any case, building products has become much easier from a technical perspective, which also makes it cheaper especially for software startups.

There are a lot of products being hunted, launched, and competing for attention here.

But I still see some makers missing the very basics.

There are founders you d love to NOT work for. And then there are founders whose company you wouldn t want to leave, no matter what.

A big part of that comes down to people, their values, and their character.

And often, that starts with the founder.

Six months ago, our team was obsessed with making Murror's AI more intelligent. Better pattern recognition, deeper emotional analysis, more insightful reflections. Every sprint, we'd ship something that made the AI sound smarter.

And our users started disengaging.

"Data portability doesn't make money."

I heard this for years - from market leaders, from VCs, from people I respected.

"This is a regulatory problem, not a technical one." "This is a feature, not a product." "People don't pay for idealistic things."

<<Back story>>
In 2019, my team and I went deep into Self-Sovereign Identity: wrote research papers, ran experiments, and found ourselves at the intersection of data, identity, and web3. Right at that intersection lay data portability and sovereignty: the ability to own your data and take it anywhere on the internet.

AI is incredibly good, I d even say almost perfect.

And for many people, that uniformity of perfect templates is starting to feel annoying.

So far, the impression I have of this community and maker circles is that you work every day.

Which is great, it s one of the reasons I m here.

Here's a marketing problem no one prepares you for: what happens when your product's success means people stop using it?

At Murror, our best outcome is when someone works through what's been weighing on them and doesn't need to come back for a while. They journal, they process, they gain clarity and then they go live their life. That's the whole point.

Building a team or want to join a startup?

Founders, teams, and startups drop a comment if you're hiring.

We thought we were ready.

Bigger deals. Fewer customers. Better margins. That was the dream.

So we built enterprise features. SSO. Advanced permissions. Audit logs. A whole new pricing tier starting at $2,000/month.

We spent 6 months. Three engineers. One dedicated product manager. Endless meetings about "enterprise readiness."

From Jeff Wang, Prev. Windsurf CEO now President, New Enterprise at Cognition/Devin (who just raised $1B):

Today we are saying goodbye to Windsurf

and we are transforming it to Devin Desktop

Windsurf has been an absolutely amazing experience for me and the team. Though it has been rocky at times, we have seen every phase of AI coding and we want to keep embracing where things are going. That means we need to once again reorient ourselves towards a more focused goal and remove the Windsurf branding.

Believe it or not, the Windsurf brand has been around less than a year and a half, and before that, the previous name Codeium was only around a similar timeframe as well. I ve actually had to change my email every year all the way to the eventual acquisition to Cognition. In AI, most products only have a 1 year lifespan before you need to drastically change it to the next.

Devin now encompasses all our form factors, whether it s the cloud agent, the agent command center (with IDE), CLI, review, or our other products. This way we can really focus our efforts around one name. We are doubling down on our neutrality and making Devin Desktop compatible with other agents via ACP. We may be the only Switzerland of AI left and we embrace this role.

As for me, I ll be transitioning from CEO of Windsurf to Cognition s President of New Enterprise, helping open new regions and verticals, accelerating velocity, and filling in gaps as usual.

The story of Windsurf doesn t end here, it continues on as part of Devin s journey.

I still rock Windsurf and love it, despite all the handwringing about pricing.

2 significant things are circulating on the internet simultaneously this week:

• A tweet about how a human beat a machine (a 10-hour livestream in which a human sorted packages and compared it to the productivity of a robot).

• Or an announcement of a Microsoft AI chief that predicts AI will automate most white-collar work within 18 months.

While I don't think there will be a complete replacement of humans in either case, I do think we will see mass layoffs. My guess is that it will be white-collar jobs first.

That's a lot of AGI, with some substantial firepower:

Anthropic has raised $65 billion in Series H funding led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, valuing the company at $965 billion post-money.

From Coatue, who lead the round:

...what s changed since three months ago (the last round was only three months ago, believe it or not)?

Three things:

1. We ve seen the shift from co-pilots that help make us more efficient to agents actually doing long-form work tasks on our behalf. This has meant an explosion in both productivity and ROI for customers.

2. Anthropic introduced Mythos Preview, a whole new model class that demonstrates a step change in intelligence level as seen in the METR chart below. It not only shows dramatic improvement to coding and long-running agents, it is also defining a new frontier for security.

3. And finally > use cases spreading from coding to everything else.

On a Tuesday, the first enterprise question is usually not can you capture AI code? It s who can see the records, how long do they live, and what happens when a policy blocks a change?

That s the part LineageLens is built for. Base gives you local capture. Lite gives a shared team record. Plus and Max move the data into a backend where auth, permissions, retention, and policy live next to the provenance records instead of around them.

The useful thing here is not another dashboard. It s a self-hosted record of prompt, model, tool, file, and outcome that engineering, security, and platform teams can actually govern on their own infrastructure.

I keep seeing AI governance tools start with visibility, then discover that the real enterprise questions are identity, retention, and review. If the record cannot be scoped, retained, and exported on your side, it is not really governable.

There seems to have two types of developers:

• Human in the loop: Those who like to control the behavior of their agents as it works, looking at the context usage, reading reasoning blocks, and approving individual file edits.

• Agent first: Those who prefer to review the output of agents, rather than individual actions, and run one or more sessions in parallel.

What type of developer are you when working with AI coding agents?